AI Engine Hierarchical Decomposition for Training Efficiency
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Solution Overview
Problem
Deep reinforcement learning faces challenges in efficiently training AI models due to the difficulty in incorporating prior learning and knowledge, especially in complex tasks with sparse rewards, leading to longer training times and suboptimal policy or value function learning, particularly in high-dimensional spaces like robotics.
Innovation Solution
The AI engine employs a hierarchical-decomposition deep reinforcement technique, integrating dynamic programming and policy optimization algorithms to automatically assemble learning topologies, allowing for parallel training of individual actions and using simpler reward functions, thereby speeding up the training process by breaking down complex tasks into smaller, manageable concepts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep reinforcement learning is used to train AI models for complex tasks, then the AI model can learn robust policies and value functions, but the training time becomes excessively long due to the need to explore large state spaces and the sparsity of reward signals
Solution Approach 1:
The patent segments the complex reinforcement learning task into multiple hierarchical levels. High-level concepts learn abstract policies through exploration, while low-level concepts learn specific actions. This segmentation allows parallel training of different concepts, significantly reducing overall training time while maintaining robust policy learning through the hierarchical structure.
Solution Approach 2:
The patent implements preliminary action by pre-defining the hierarchical structure of concepts before training begins. The system pre-identifies which concepts should be learned at which hierarchical levels, allowing the training process to start with high-level concepts that provide guidance for lower-level concepts, thereby reducing the exploration time needed in the full state space.
2Adaptability or versatility
If the AI model is trained to solve a wide variety of problem types, then the model's adaptability increases, but the training complexity and time required increase significantly
Solution Approach 1:
The patent implements universality by creating a hierarchical concept framework that can be applied across multiple problem types. High-level concepts serve as universal abstractions that can guide learning across different tasks, while low-level concepts can be specialized for specific problem types. This allows the same hierarchical framework to handle diverse problems without requiring separate training systems for each.
Solution Approach 2:
The patent applies local quality by allowing different concepts at different hierarchical levels to use different learning algorithms and reward functions appropriate to their specific function. High-level concepts may use exploration-based learning while low-level concepts use exploitation-based learning, optimizing each level for its specific role while maintaining overall system versatility.
3Productivity
If the AI model incorporates prior learning and knowledge, then training efficiency improves, but it becomes difficult to integrate existing knowledge into new training scenarios
Solution Approach 1:
The patent implements the nested doll principle by organizing knowledge hierarchically where high-level concepts contain and guide low-level concepts. Prior learning can be stored as high-level concepts that provide general guidance, while new specific knowledge is learned as low-level concepts nested within the hierarchical structure. This allows efficient integration of prior knowledge without requiring complete retraining, as the hierarchical framework naturally accommodates both general and specific knowledge.
Data Source
AI summary
The AI engine has a first module that chooses from a library of algorithms to use when automatically assembling and building different learning topologies to solve different concepts making up a resulting AI model. The AI engine may integrate both i) one or more dynamic programming training algorithms and ii) one or more policy optimization algorithms, to build the different learning topologies to solve the different concepts contained with an AI model in order to solve a wide variety of problem types. Each concept contained in the AI model can use a most appropriate approach for achieving a mission of that concept. A learning topology representing a first concept can be built by the first module with a first dynamic programming training algorithm, while a learning topology representing a second concept in the same AI model can be built by the first module with a first policy optimization algorithm.